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Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines
Joint Authors
Peng, Pingan
He, Zhengxiang
Wang, Liguan
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-11
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines.
The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events.
The visual scanning process is a time-consuming task and requires experience.
Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) by using only Mel-frequency cepstral coefficient (MFCC) features extracted from the waveform.
The detailed implementation of our proposed method is described.
The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China).
A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing.
The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines.
American Psychological Association (APA)
Peng, Pingan& He, Zhengxiang& Wang, Liguan. 2019. Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines. Shock and Vibration،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1211352
Modern Language Association (MLA)
Peng, Pingan…[et al.]. Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines. Shock and Vibration No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1211352
American Medical Association (AMA)
Peng, Pingan& He, Zhengxiang& Wang, Liguan. Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1211352
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211352